Authors:
Za'er Salim Abo-Hammour
1
;
Mohammad Suleiman Saraireh
1
and
Othman M-K. Alsmadi
2
Affiliations:
1
Jordan University;Amman-Jordan Mutah University, Jordan
;
2
Jordan University, Jordan
Keyword(s):
Robot manipulators, Singularity avoidance, Cartesian path generation, Inverse kinematics problem, Continuous genetic algorithms, Conventional genetic algorithms.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Mobile Robots and Autonomous Systems
;
Network Robotics
;
Robot Design, Development and Control
;
Robotics and Automation
;
Soft Computing
Abstract:
In this paper, a comparative study between the continuous and the conventional GAs for the solution of Cartesian path generation problems of robot manipulators is performed. The difference between both algorithms lies in the ways in which initialization phase, the crossover operator, and the mutation operator are applied. Generally, the operators of the Continuous Genetic Algorithms (CGA) are of global nature, i.e., applied at the joint’s path level, while those of conventional GA are of local nature, i.e., applied at the path point level. It was concluded from the simulations included that CGAs have several advantages over conventional GAs when applied to the path generation problems; first, the joints’ paths obtained using the conventional GA are found to be of highly oscillatory nature resulting in very large net joints displacements consuming more energy and requiring more time. This problem is totally avoided in CGA where the resulting joints’ paths are smooth. Second, the CGA h
as faster convergence speed (number of generations required for convergence) than the conventional GA. Third, the average execution time per generation in the conventional GA is two to three times that in the CGA. This is due to the fact that the conventional GA requires a coding process, which is not the case in the CGA. Fourth, the memory requirements of the conventional GA are higher than those of the CGA because the former uses genotype and phenotype representations while the later utilizes only the phenotype representation.
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